diff --git a/farm/data_handler/input_features.py b/farm/data_handler/input_features.py
index a24335c0d..ca0850a77 100644
--- a/farm/data_handler/input_features.py
+++ b/farm/data_handler/input_features.py
@@ -358,6 +358,8 @@ def sample_to_features_qa(sample, tokenizer, max_seq_len, answer_type_list=None,
     # seq_2_start_t is the index of the first token in the second text sequence (e.g. passage)
     if tokenizer.__class__.__name__ in ["RobertaTokenizer", "XLMRobertaTokenizer"]:
         seq_2_start_t = get_roberta_seq_2_start(input_ids)
+    elif tokenizer.__class__.__name__ == "CamembertTokenizer":
+        seq_2_start_t = get_camembert_seq_2_start(input_ids)
     else:
         seq_2_start_t = segment_ids.index(1)
 
@@ -514,6 +516,17 @@ def get_roberta_seq_2_start(input_ids):
     second_backslash_s = input_ids.index(2, first_backslash_s + 1)
     return second_backslash_s + 1
 
+def get_camembert_seq_2_start(input_ids):
+    # CamembertTokenizer.encode_plus returns only zeros in token_type_ids (same as RobertaTokenizer).
+    # This is another way to find the start of the second sequence (following get_roberta_seq_2_start)
+    # Camembert input sequences have the following
+    # format: <s> P1 </s> </s> P2 </s>
+    # <s> has index 5 and </s> has index 6. To find the beginning of the second sequence, this function first finds
+    # the index of the second </s>
+    first_backslash_s = input_ids.index(6)
+    second_backslash_s = input_ids.index(6, first_backslash_s + 1)
+    return second_backslash_s + 1
+
 def sample_to_features_squadOLD(
     sample, tokenizer, max_seq_len, doc_stride, max_query_length, tasks,
 ):
diff --git a/farm/modeling/adaptive_model.py b/farm/modeling/adaptive_model.py
index c3c9d8561..aada3c358 100644
--- a/farm/modeling/adaptive_model.py
+++ b/farm/modeling/adaptive_model.py
@@ -256,7 +256,6 @@ def __init__(
         self.lm_output_types = (
             [lm_output_types] if isinstance(lm_output_types, str) else lm_output_types
         )
-        assert len(self.lm_output_types) == len(self.prediction_heads)
         self.log_params()
         # default loss aggregation function is a simple sum (without using any of the optional params)
         if not loss_aggregation_fn:
diff --git a/farm/modeling/language_model.py b/farm/modeling/language_model.py
index 32de9ee5e..f76cda425 100644
--- a/farm/modeling/language_model.py
+++ b/farm/modeling/language_model.py
@@ -41,13 +41,13 @@
 from transformers.modeling_xlm_roberta import XLMRobertaModel, XLMRobertaConfig
 from transformers.modeling_distilbert import DistilBertModel, DistilBertConfig
 from transformers.modeling_electra import ElectraModel, ElectraConfig
+from transformers.modeling_camembert import CamembertModel, CamembertConfig
 from transformers.modeling_utils import SequenceSummary
 from transformers.tokenization_bert import load_vocab
 
 from farm.modeling import wordembedding_utils
 from farm.modeling.wordembedding_utils import s3e_pooling
 
-
 # These are the names of the attributes in various model configs which refer to the number of dimensions
 # in the output vectors
 OUTPUT_DIM_NAMES = ["dim", "hidden_size", "d_model"]
@@ -134,6 +134,8 @@ def load(cls, pretrained_model_name_or_path, n_added_tokens=0, language_model_cl
                     language_model_class = 'XLMRoberta'
                 elif 'roberta' in pretrained_model_name_or_path:
                     language_model_class = 'Roberta'
+                elif 'camembert' in pretrained_model_name_or_path or 'umberto' in pretrained_model_name_or_path:
+                    language_model_class = "Camembert"
                 elif 'albert' in pretrained_model_name_or_path:
                     language_model_class = 'Albert'
                 elif 'distilbert' in pretrained_model_name_or_path:
@@ -234,7 +236,17 @@ def _infer_language_from_name(cls, name):
             "multilingual",
         )
         matches = [lang for lang in known_languages if lang in name]
-        if len(matches) == 0:
+        if "camembert" in name:
+            language = "french"
+            logger.info(
+                f"Automatically detected language from language model name: {language}"
+            )
+        elif "umberto" in name:
+            language = "italian"
+            logger.info(
+                f"Automatically detected language from language model name: {language}"
+            )
+        elif len(matches) == 0:
             language = "english"
             logger.warning(
                 "Could not automatically detect from language model name what language it is. \n"
@@ -242,11 +254,13 @@ def _infer_language_from_name(cls, name):
                 "\t If not: Init the language model by supplying the 'language' param."
             )
         elif len(matches) > 1:
-            raise ValueError(
+            logger.warning(
                 "Could not automatically detect from language model name what language it is.\n"
                 f"\t Found multiple matches: {matches}\n"
                 "\t Please init the language model by manually supplying the 'language' as a parameter.\n"
+                f"\t Using {matches[0]} as language parameter for now.\n"
             )
+            language = matches[0]
         else:
             language = matches[0]
             logger.info(
@@ -879,7 +893,7 @@ def forward(
         )
         # XLNet also only returns the sequence_output (one vec per token)
         # We need to manually aggregate that to get a pooled output (one vec per seq)
-        #TODO verify that this is really doing correct pooling
+        # TODO verify that this is really doing correct pooling
         pooled_output = self.pooler(output_tuple[0])
 
         if self.model.output_hidden_states == True:
@@ -1282,3 +1296,47 @@ def enable_hidden_states_output(self):
     def disable_hidden_states_output(self):
         self.model.config.output_hidden_states = False
 
+
+class Camembert(Roberta):
+    """
+    A Camembert model that wraps the HuggingFace's implementation
+    (https://github.com/huggingface/transformers) to fit the LanguageModel class.
+    """
+    def __init__(self):
+        super(Camembert, self).__init__()
+        self.model = None
+        self.name = "camembert"
+
+    @classmethod
+    def load(cls, pretrained_model_name_or_path, language=None, **kwargs):
+        """
+        Load a language model either by supplying
+
+        * the name of a remote model on s3 ("camembert-base" ...)
+        * or a local path of a model trained via transformers ("some_dir/huggingface_model")
+        * or a local path of a model trained via FARM ("some_dir/farm_model")
+
+        :param pretrained_model_name_or_path: name or path of a model
+        :param language: (Optional) Name of language the model was trained for (e.g. "german").
+                         If not supplied, FARM will try to infer it from the model name.
+        :return: Language Model
+
+        """
+        camembert = cls()
+        if "farm_lm_name" in kwargs:
+            camembert.name = kwargs["farm_lm_name"]
+        else:
+            camembert.name = pretrained_model_name_or_path
+        # We need to differentiate between loading model using FARM format and Pytorch-Transformers format
+        farm_lm_config = Path(pretrained_model_name_or_path) / "language_model_config.json"
+        if os.path.exists(farm_lm_config):
+            # FARM style
+            config = CamembertConfig.from_pretrained(farm_lm_config)
+            farm_lm_model = Path(pretrained_model_name_or_path) / "language_model.bin"
+            camembert.model = CamembertModel.from_pretrained(farm_lm_model, config=config, **kwargs)
+            camembert.language = camembert.model.config.language
+        else:
+            # Huggingface transformer Style
+            camembert.model = CamembertModel.from_pretrained(str(pretrained_model_name_or_path), **kwargs)
+            camembert.language = cls._get_or_infer_language_from_name(language, pretrained_model_name_or_path)
+        return camembert
diff --git a/farm/modeling/tokenization.py b/farm/modeling/tokenization.py
index ee6c7f010..d012cb0e6 100644
--- a/farm/modeling/tokenization.py
+++ b/farm/modeling/tokenization.py
@@ -31,6 +31,7 @@
 from transformers.tokenization_utils import PreTrainedTokenizer
 from transformers.tokenization_xlm_roberta import XLMRobertaTokenizer
 from transformers.tokenization_xlnet import XLNetTokenizer
+from transformers.tokenization_camembert import CamembertTokenizer
 
 from farm.modeling.wordembedding_utils import load_from_cache, EMBEDDING_VOCAB_FILES_MAP, run_split_on_punc
 
@@ -69,6 +70,8 @@ def load(cls, pretrained_model_name_or_path, tokenizer_class=None, **kwargs):
                 tokenizer_class = "XLMRobertaTokenizer"
             elif "roberta" in pretrained_model_name_or_path.lower():
                 tokenizer_class = "RobertaTokenizer"
+            elif "camembert" in pretrained_model_name_or_path.lower() or "umberto" in pretrained_model_name_or_path:
+                tokenizer_class = "CamembertTokenizer"
             elif "distilbert" in pretrained_model_name_or_path.lower():
                 tokenizer_class = "DistilBertTokenizer"
             elif "bert" in pretrained_model_name_or_path.lower():
@@ -104,6 +107,8 @@ def load(cls, pretrained_model_name_or_path, tokenizer_class=None, **kwargs):
             ret = ElectraTokenizer.from_pretrained(pretrained_model_name_or_path, **kwargs)
         elif tokenizer_class == "EmbeddingTokenizer":
             ret = EmbeddingTokenizer.from_pretrained(pretrained_model_name_or_path, **kwargs)
+        elif tokenizer_class == "CamembertTokenizer":
+            ret = CamembertTokenizer._from_pretrained(pretrained_model_name_or_path, **kwargs)
         if ret is None:
             raise Exception("Unable to load tokenizer")
         else:
diff --git a/readme.rst b/readme.rst
index 89b56e984..122085379 100644
--- a/readme.rst
+++ b/readme.rst
@@ -66,7 +66,7 @@ Core features
 - Simple **deployment** and **visualization** to showcase your model
 
 +------------------------------+-------------------+-------------------+-------------------+-------------------+-------------------+-------------------+
-| Task                         |      BERT         |  RoBERTa          |  XLNet            |  ALBERT           |  DistilBERT       |  XLMRoBERTa       |
+| Task                         |      BERT         |  RoBERTa*         |  XLNet            |  ALBERT           |  DistilBERT       |  XLMRoBERTa       |
 +==============================+===================+===================+===================+===================+===================+===================+
 | Text classification          | x                 |  x                |  x                |  x                |  x                |  x                |
 +------------------------------+-------------------+-------------------+-------------------+-------------------+-------------------+-------------------+
@@ -89,6 +89,8 @@ Core features
 | Passage Ranking              | x                 |  x                |  x                |  x                |  x                |  x                |
 +------------------------------+-------------------+-------------------+-------------------+-------------------+-------------------+-------------------+
 
+\* including CamemBERT and UmBERTo
+
 ****NEW**** Interested in doing Question Answering at scale? Checkout `Haystack <https://github.com/deepset-ai/haystack>`_!
 
 Resources